This is the first of three articles that treat a new approach for the Path Finding. It's a part of the studies and experience I made in the last years, in the fields of the Artificial Intelligence for Games, Philosophy and Evolutionary Psychology.

The current panorama of the Path Finding

All the Artificial Intelligence modules for Videogames are made by using models thought several years ago. Some of those models are rather old, taking into consideration the fast growing of the other software modules of videogames.

For example, even I didn’t found any source that specifies when the Navigation Mesh (or Navmesh) has been implemented for the first time, I can state that this model is used at least from the 2004 (and then 10 years ago). It is, together with the Navigation Grid, the most used and, practically, the most advanced approach to obtain the graph for making paths for videogames.

Nonetheless, the Waypoint Navigation is still used in a multitude of simple games, and is even older than the Navmesh. If we compare the improvements in the AI modules with the ones of Graphic modules (I mean the ones that render the game), we could see that, referring to when the waypoint navigation has been used for the first times, the graphics were at level of the CGA technology. The evolution made by the Graphic module, in these years, is enormous.

Ok, you could say that computer graphics has been largely helped by the evolution of the hardware. The problem, though, remains: in the last 10 years, if we avoid to mention the OCC model (relative to the psychological approach to the intelligent agents) and some other agent-based approach that, by the way, are still practically unused in videogames, we can’t say to have witnessed something that could be defined as really new, or a strong, sharable improvement.

The reason? Mainly because, despite the advent of the cognitive science, the approach to the artificial intelligence topic by the insiders is never changed in decades. How the path finding of the videogames of the future will be? Is it possible that it will have modules made on models that we have from decades?

Personally, I reckon that the videogames of the future will be far different from the currents. The problem for developers is not how they will be, but how to build AI models able to support new features. I had a different approach, respect to the classic one, about the “intelligence” topic. I started from the consciousness that we humans are not a species fallen from the sky, completely different from all the other species of Nature on Earth. This brought me to know the Evolutionary Psychology, from which I profitably tapped several theories that, I’m sure, will help AI to develop the first, real, virtual living being.

A topic question: what is intelligence?

What is Intelligence, speaking about what is important for videogames? Intelligence is not more than a tool to improve the human ability to manage the environment. It’s the more powerful tool ever made by natural selection on Earth, but still a tool like the elephant tusks, or the opposable thumbs we share with the other primates. The Evolutionary Psychology calls them Evolutionary Adaptations. There are a lot of studies and findings, in Neurosciences and Psychology that state the fact that emotions take part of the human (and animal) intelligence, and without them the sole logic is useless.

This way to think at intelligence changes dramatically the approach to any problem you find in videogames, and with any other human or animal simulation. For instance, my approach to the path finding issue takes into consideration the fact that humans (and animals) have their own way to memorize maps in mind. The result of this memorization often doesn’t correspond to the actual map, more often than we would admit.

Even the approach to the navigation for building the graph is completely different. In my opinion, it should take into consideration how we move toward a physical target. This approach carries several new features and performance improvements, in respect to the ones currently adopted.

I try in few words to explain some of the main thoughts that are at the base of this new approach. First of all, I need to title the parts in which I divide the news.

First Topic - Navigation for innovative games

Please think about a game like “I am alive”, "Spiderman", “Tomb Rider”, “Prince of Persia”, “Assassin’s creed” or one of a lot of others where the player’s character is able to climb, jump and make other unusual actions. In almost all the titles of this genre, there are some unnatural behaviours done by NPCs. One of the most evident is when NPCs want to catch/kill the player and, in order to do that, “decide” not to climb and follow the PC. They only expect the player to finish his climbing actions in order to attack him, or try to shoot him from the walkable area when it’s climbing.

This is not a logical and biological behaviour, because unless all the NPCs know the buildings around them like their shoes, they can’t be sure that the PC could flee from their hands. In order to reproduce a correct human behaviour, NPCs, for example, should use the simple tactic of the pincer: some of them follows the thief/enemy/PC by climbing behind him, while the others try to run and block his way out. How to do that, though, if the Path Finding Navigation system can’t use walls as “usable areas”? Videogame programmers rarely make their NPCs able to climb and follow the climbing player. This is because, in order to do that, they should customize the path finding module, making it more complex and often not usable for other videogames in which the player only has to walk and run.

A new approach, that I baptized “Biological Path Finding” (BPF), resolve the problem at its root. There should be focal points (automatically generated) only in the places where two micro-environments meet. This brings to have a focal point even where the way to proceed changes because the different micro-environment is vertical, that is a wall or some other “climbable” surface. Each line that comes from a node will acquire a weight (talking with an A* approach) that will be calculated in base of the ability of each “kind of NPC” to traverse it, by climbing, swimming, walking etc. This solution involves a more general management of NPCs, whom should be categorized in Species.

The good news is that, with this approach, you can have a different best path for each Species of NPC, while each NPC is able to move along the level based on its own ability. The better performance in elaborating the paths thanks to a lower amount of nodes, respect to the current approaches, mitigate the fact that the path elaboration, with this approach, will be done for each species. Unless the game uses dozens of different species (I never found a game like that!), the total amount of time for elaborating the paths off-line should be not higher than the ones of the current approaches, but giving us more features and wider solutions to adopt in videogames.

Yes, it’s something more complicated than a mere, simple path navigation system (do and use), like most of the current videogames have. Nonetheless, videogames are already evolved from the first ones like Invaders or Pacman, and we can surely say that all the main titles in genres like RPG, MMORPG, Shooters and Strategy are very complex per se, so that the cost for having new features for NPCs could be paid without too much regret.

The need to ease the run-time elaboration

In the last years, more and more videogames contain larger levels. It’s obvious that the larger the level, the harder is remaining into acceptable performance when NPCs calculate their paths to catch the target and the number of NPCs is higher.

I know that several videogame developers recognize that problem as one of the most time-consuming during the development. The fact is that we continue to think too logically to the issues that involve human simulations. If we think, instead, to have a person in place of each NPC, we should then linger on to the way with which humans think about the path finding, speaking from the point of view of the Psychology.

I don’t want to write down here a book again (I already made it, and in the next month will be published), so I need to condensate some thoughts in a shorter version. When a human tries to think about a larger zone and how to reach his destination, he usually has some mental images about the most important, for him, parts of the environment (blocks of a city, for example), and then think about each zone as a black box. This approach helps a lot, even with our path finding issue, because it avoids calculating the path at the highest detail. One of the studies in Psychology that better analyse the issues related to paths traveled by humans has been done by Lynch (1960).

The double dimension for path finding has been already used in some games. The fact is that in the Biological Path Finding it fits the overall approach, being one of the tools that derive directly from a psychological study of the human behaviour.

What this approach brings is the fact that you save time in making only the calculation that you really need. In fact, are you sure that the NPC will travel all the path? How high is the possibility that the NPC will be killed along the path, or will find a good reason to find a new path again or, in any case, do something different? In these cases, the calculation of the overall path in a large level has been made in vain. The approach of breaking large calculations into several, smaller ones made “along the road” by NPCs should be used widely, because it’s a good way to enhance the performance. In any case, this lets the NPC be more “human” without making heavy the overall performance.

Then, the BPF takes use of two dimensions of Path Finding: the District and the Zone Path Finding. The District Path Finding is the one I explained in the previous paragraphs, the other is the one that resolve the need to walk along a short path, in order to catch a closer target or exit from the district (if necessary) from the side required by the selection of the “correct” (not the best!) macro-path found by the District Path Finding.

Even at that level, the approach is completely different from the usual. It tries to minimize the amount of micro-paths that has to be kept into consideration. Humans, in fact, take a decision about the path to use, in order to move toward a closer target, by looking at the target (or in direction of it) and check only the paths that have their first focal point not distant, in terms of angular degrees, respect to the direct direction toward the target.

This way to select the path has been already addressed by other researchers, for example by Douglas Jon Demyen (2007) in his thesis.

Conclusion

In my next articles, I’ll continue to talk about a Biological Path Finding approach, and more precisely talking about the other five topics already named in this article.

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About the Author(s)

Giovanni Guarino is a AI senior consultant, with twenty years of experience in AI for Industries, and with a deep love for videogames. He made with some friends an indie company that developed 5 videogames on demand, from the 2009 and 2011, and some others made by himself for some friends and his daughters.

He is concluding one book and two academic papers, whose topics are innovative AI modules for making better human simulations and videogames.

At the start of the article I paused and tried to imagine how I would implement an innovative path-finding algorithm in my non-existent (but nevertheless awesome) game project. I was gratified to discover that the simple model I envisioned lines up exactly with what the author describes. I'm going to take encouragement from that. Maybe I'll start that game project sooner than later.
I'm very much looking forward to the rest of this series.

Very interesting approach. It appears English is not your first language, and, in a few places in the article, it's not very clear what you are trying to say. However, your message gets through, nonetheless.

I'm not seeing anything novel here. The so-called "BPF" is just hierarchical path-finding combined with predicated/filtered path-finding, and neither of those techniques nor their combination are new. Or am I missing some key element of this article (totally likely) ?

I think that's probably it's useful that you take a look along the whole article, because in the other parts you can see a lot of differences with the other types of path finding. Anyway, you're right when you say that it's not totally new. As I already wrote in the second part of the article, some parts of the model have already been used in other contexts, but here are for the first time part of a whole, organic model, and for the first time are confirmed by some psychologic theories.

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